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Identification of clinical factors related to prediction of alcohol use disorder from electronic health records using feature selection methods
BACKGROUND: High dimensionality in electronic health records (EHR) causes a significant computational problem for any systematic search for predictive, diagnostic, or prognostic patterns. Feature selection (FS) methods have been indicated to be effective in feature reduction as well as in identifyin...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9686074/ https://www.ncbi.nlm.nih.gov/pubmed/36424597 http://dx.doi.org/10.1186/s12911-022-02051-w |
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author | Ebrahimi, Ali Wiil, Uffe Kock Naemi, Amin Mansourvar, Marjan Andersen, Kjeld Nielsen, Anette Søgaard |
author_facet | Ebrahimi, Ali Wiil, Uffe Kock Naemi, Amin Mansourvar, Marjan Andersen, Kjeld Nielsen, Anette Søgaard |
author_sort | Ebrahimi, Ali |
collection | PubMed |
description | BACKGROUND: High dimensionality in electronic health records (EHR) causes a significant computational problem for any systematic search for predictive, diagnostic, or prognostic patterns. Feature selection (FS) methods have been indicated to be effective in feature reduction as well as in identifying risk factors related to prediction of clinical disorders. This paper examines the prediction of patients with alcohol use disorder (AUD) using machine learning (ML) and attempts to identify risk factors related to the diagnosis of AUD. METHODS: A FS framework consisting of two operational levels, base selectors and ensemble selectors. The first level consists of five FS methods: three filter methods, one wrapper method, and one embedded method. Base selector outputs are aggregated to develop four ensemble FS methods. The outputs of FS method were then fed into three ML algorithms: support vector machine (SVM), K-nearest neighbor (KNN), and random forest (RF) to compare and identify the best feature subset for the prediction of AUD from EHRs. RESULTS: In terms of feature reduction, the embedded FS method could significantly reduce the number of features from 361 to 131. In terms of classification performance, RF based on 272 features selected by our proposed ensemble method (Union FS) with the highest accuracy in predicting patients with AUD, 96%, outperformed all other models in terms of AUROC, AUPRC, Precision, Recall, and F1-Score. Considering the limitations of embedded and wrapper methods, the best overall performance was achieved by our proposed Union Filter FS, which reduced the number of features to 223 and improved Precision, Recall, and F1-Score in RF from 0.77, 0.65, and 0.71 to 0.87, 0.81, and 0.84, respectively. Our findings indicate that, besides gender, age, and length of stay at the hospital, diagnosis related to digestive organs, bones, muscles and connective tissue, and the nervous systems are important clinical factors related to the prediction of patients with AUD. CONCLUSION: Our proposed FS method could improve the classification performance significantly. It could identify clinical factors related to prediction of AUD from EHRs, thereby effectively helping clinical staff to identify and treat AUD patients and improving medical knowledge of the AUD condition. Moreover, the diversity of features among female and male patients as well as gender disparity were investigated using FS methods and ML techniques. |
format | Online Article Text |
id | pubmed-9686074 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-96860742022-11-25 Identification of clinical factors related to prediction of alcohol use disorder from electronic health records using feature selection methods Ebrahimi, Ali Wiil, Uffe Kock Naemi, Amin Mansourvar, Marjan Andersen, Kjeld Nielsen, Anette Søgaard BMC Med Inform Decis Mak Research BACKGROUND: High dimensionality in electronic health records (EHR) causes a significant computational problem for any systematic search for predictive, diagnostic, or prognostic patterns. Feature selection (FS) methods have been indicated to be effective in feature reduction as well as in identifying risk factors related to prediction of clinical disorders. This paper examines the prediction of patients with alcohol use disorder (AUD) using machine learning (ML) and attempts to identify risk factors related to the diagnosis of AUD. METHODS: A FS framework consisting of two operational levels, base selectors and ensemble selectors. The first level consists of five FS methods: three filter methods, one wrapper method, and one embedded method. Base selector outputs are aggregated to develop four ensemble FS methods. The outputs of FS method were then fed into three ML algorithms: support vector machine (SVM), K-nearest neighbor (KNN), and random forest (RF) to compare and identify the best feature subset for the prediction of AUD from EHRs. RESULTS: In terms of feature reduction, the embedded FS method could significantly reduce the number of features from 361 to 131. In terms of classification performance, RF based on 272 features selected by our proposed ensemble method (Union FS) with the highest accuracy in predicting patients with AUD, 96%, outperformed all other models in terms of AUROC, AUPRC, Precision, Recall, and F1-Score. Considering the limitations of embedded and wrapper methods, the best overall performance was achieved by our proposed Union Filter FS, which reduced the number of features to 223 and improved Precision, Recall, and F1-Score in RF from 0.77, 0.65, and 0.71 to 0.87, 0.81, and 0.84, respectively. Our findings indicate that, besides gender, age, and length of stay at the hospital, diagnosis related to digestive organs, bones, muscles and connective tissue, and the nervous systems are important clinical factors related to the prediction of patients with AUD. CONCLUSION: Our proposed FS method could improve the classification performance significantly. It could identify clinical factors related to prediction of AUD from EHRs, thereby effectively helping clinical staff to identify and treat AUD patients and improving medical knowledge of the AUD condition. Moreover, the diversity of features among female and male patients as well as gender disparity were investigated using FS methods and ML techniques. BioMed Central 2022-11-23 /pmc/articles/PMC9686074/ /pubmed/36424597 http://dx.doi.org/10.1186/s12911-022-02051-w Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Research Ebrahimi, Ali Wiil, Uffe Kock Naemi, Amin Mansourvar, Marjan Andersen, Kjeld Nielsen, Anette Søgaard Identification of clinical factors related to prediction of alcohol use disorder from electronic health records using feature selection methods |
title | Identification of clinical factors related to prediction of alcohol use disorder from electronic health records using feature selection methods |
title_full | Identification of clinical factors related to prediction of alcohol use disorder from electronic health records using feature selection methods |
title_fullStr | Identification of clinical factors related to prediction of alcohol use disorder from electronic health records using feature selection methods |
title_full_unstemmed | Identification of clinical factors related to prediction of alcohol use disorder from electronic health records using feature selection methods |
title_short | Identification of clinical factors related to prediction of alcohol use disorder from electronic health records using feature selection methods |
title_sort | identification of clinical factors related to prediction of alcohol use disorder from electronic health records using feature selection methods |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9686074/ https://www.ncbi.nlm.nih.gov/pubmed/36424597 http://dx.doi.org/10.1186/s12911-022-02051-w |
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